Academic data (i.e., Thesis, Dissertation) encompasses aspects of a whole society, as well as its scientific knowledge. There is a wealth of information to be explored by computational models, and that can be positive for society. Machine learning models in particular, have an increasing need for training data, that are efficient and of considerable size. Its use in the area of natural language processing (NLP) is pervasive in many different tasks. This work makes the effort of collecting, constructing, analyzing and training of models for the biggest known academic corpus in the Portuguese language. Word embeddings, bag of words and transformers models have been trained. The Bert-Academico has shown the better result, with 77 percent of f1-score in Great area of knowledge and 63 percent in knowledge area classification of Thesis and Dissertation. A semantic analysis of the academic corpus is made through topic modelling, and an unprecedented visualization of the knowledge areas is presented. Lastly, an application that uses the trained models is showcased, the SucupiraBot.